---
title: "Crna Gora COVID-19"
author: "W"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
library(ggplot2)
library(plotly)
library(plyr)
library(flexdashboard)

library(tidyr)

mne <- read.csv("/Users/Dovla/Desktop/covidMNE2003.csv")

# Make some noisily increasing data
set.seed(955)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
                  xvar = 1:20 + rnorm(20,sd=3),
                  yvar = 1:20 + rnorm(20,sd=3))
```

Samoizolacija
=======================================================================

Podaci JIZCG
=======================================================================

Row
-----------------------------------------------------------------------

### Samoizolacija

```{r}
articles <- 10
valueBox(articles, icon = "fa-home", color = "success")
```

### COVID-19 slucajevi

```{r}
comments <- 10
valueBox(comments, icon = "fa-notes-medical", color = "warning")
```

### Umrli

```{r}
spam <- 10
valueBox(spam, 
         icon = "fa-battery-empty",
         color = "danger")
```


Row
-----------------------------------------------------------------------

### Evolucija COVID-19 slucajeva 

```{r}
myvars <- c("Day","Infected", "Tested")
mne1 <- mne[myvars]
mne2 <- mne1 %>%
  pivot_longer(-Day, names_to = "series", values_to = "count")


p1 <- ggplot(data=mne2, aes(x=Day, y=count, group=series)) + geom_line()
ggplotly(p1)



#p <- ggplot(dat, aes(x=xvar, y=yvar)) +
#            geom_point(shape=1)      # Use hollow circles
#ggplotly(p)
```


### Stopa rasta novih COVID-19 slucajeva

```{r}
p2 <- ggplot(data=mne, aes(x=Day, y=InfectedNew, group=1)) + geom_line()
ggplotly(p2)

```

Row
-----------------------------------------------------------------------

### Novi COVID-19 slucajevi

```{r}
p3 <- ggplot(data=mne, aes(x=Day, y=InfectedNew)) + geom_bar(stat="identity")
ggplotly(p3)
```

### Evolucija od prvog COVID-19 slucaja

```{r}
n <- 20
x1 <- rnorm(n); x2 <- rnorm(n)
y1 <- 2 * x1 + rnorm(n)
y2 <- 3 * x2 + (2 + rnorm(n))
A <- as.factor(rep(c(1, 2), each = n))
df <- data.frame(x = c(x1, x2), y = c(y1, y2), A = A)
fm <- lm(y ~ x + A, data = df)

p <- ggplot(data = cbind(df, pred = predict(fm)), aes(x = x, y = y, color = A))
p <- p + geom_point() + geom_line(aes(y = pred))
ggplotly(p)
```

SIRD model za predvidjanje
=======================================================================

Twitter analiza
=======================================================================